Autonomous Multi-Agent AI System for Distributed Healthcare Decision Coordination

Abstract

The increasing complexity of modern healthcare systems requires coordinated decision-making across multiple stakeholders, including clinicians, hospitals, and intelligent systems. This paper presents an Autonomous Multi-Agent AI System for Distributed Healthcare Decision Coordination. The proposed framework utilizes multiple intelligent agents, each responsible for specific healthcare tasks such as diagnosis, treatment recommendation, resource allocation, and patient monitoring. These agents communicate and collaborate using decentralized protocols to achieve global optimization of healthcare outcomes. Reinforcement learning and game-theoretic approaches are employed to enable agents to learn optimal strategies in dynamic and uncertain environments. The system integrates multi-modal healthcare data, including clinical records, real-time sensor inputs, and imaging data, to support comprehensive decision-making. Additionally, conflict resolution and coordination mechanisms are incorporated to ensure consistency and reliability across agents. Experimental results demonstrate that the proposed multi-agent system improves decision efficiency, scalability, and adaptability compared to centralized approaches. Integration with prior research in healthcare analytics enhances system robustness and generalization. The study highlights the potential of multi-agent AI systems in enabling distributed, intelligent, and collaborative healthcare ecosystems for next-generation medical applications.